Regulating environmental conditions within an event venue

ABSTRACT

A venue occupant comfort system, comprises a processor that stores computer executable components stored in memory. A plurality of sensors sense ambient conditions associated with exterior and interior conditions of a venue. A context component infers or determines context of an occupant of the venue. A crowd estimation component infers, based at least in part on mining social networks, size of crowd expected at the venue. A comfort model component implicitly and explicitly trained on occupant comfort related data analyzes information from the plurality of sensors, the crowd estimation component and context component. A comfort controller adjusts environmental conditions of the venue based at least in part on output of the comfort model component. The adjustments to venue environment can optionally be differentiated by zone.

BACKGROUND

The subject disclosure generally relates to machine learning systems andin particular to utilizing machine learning systems to regulateenvironmental conditions within a venue based on predictive crowdestimation.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later.

One embodiment of the invention is a system, comprising a memory storingone or more computer readable and executable components; a processoroperably coupled to the memory and that executes the computer readableand executable components stored in the memory; the componentscomprising: a crowd estimation component that infers size of a crowdexpected to occupy the venue based in part on data obtained from one ormore social networks; a comfort model component, trained on crowdcomfort related data, that analyzes information from the crowdestimation component; and a comfort controller that adjustsenvironmental conditions of the venue interior based at least in part onoutput from the comfort model component.

Other embodiments include a computer-implemented method and a computerprogram product.

In some embodiments, elements of one or more computer-implementedmethods can be embodied in different (or a combination of) forms. Forexample, one or more elements of a computer-implemented method may beembodied (without limitation) as (or with) a system, a computer programproduct, and/or another form.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat utilizes machine learning systems, in accordance with one or moreembodiments described herein.

FIG. 2 illustrates a schematic block diagram of an example venue thatutilizes systems in accordance with one or more embodiments describedherein.

FIG. 3 illustrates an example flow diagram of a method in accordancewith one or more embodiments described herein.

FIG. 4 illustrates another example flow diagram of a method inaccordance with one or more embodiments described herein.

FIG. 5 illustrates a block diagram of an example, non-limiting system inaccordance with one or more embodiments described herein.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

One or more embodiments of the subject disclosure describes utilizingmachine learning systems to mine social network sites in connection withinferring a number of occupants that will visit a venue, and toaccordingly regulate environmental conditions within the venue toimprove occupant(s) comfort. The machine learning systems can beexplicitly or implicitly trained. For example, over time, someembodiments can employ feedback to learn from an actual number anddemographics of attendees compared to that inferred by the machinelearning system. Accordingly, over time, the machine learning systemscan converge on higher confidence levels through such learning.

Concert halls, theaters, arenas, indoor stadiums, banquet halls,auditoriums, exercise facilities, recreation centers and the like arevenues typically occupied by groups of individuals that desire to shareor participate in a common experience. Research shows that occupantcomfort within a venue might be spoiled due to various reasons,including foul air, uncomfortable temperature and humidity, air flowlevel, sound levels, lighting levels, etc. Venues are typically equippedwith various comfort facilities (e.g. air conditioners, lightingcontrol, sound control, heating, ventilation and air conditioning (HVAC)systems, etc.). There is opportunity to improve venue comfort byadjusting these facilities, but there is a requirement of externaltrigger(s) from human(s) or smart control system(s). Some technologiesare developed to automatically adjust venue temperature in reaction to asensed change in the environmental context (e.g., to activate the airconditioner when the temperature is sensed as exceeding a pre-setdesired temperature, or to activate the heating system in reaction tosensing that the temperature has dropped below a pre-set desiredtemperature). Such automatic adjustments, however, are typically notproactive, e.g., they are triggered in response to a sensed change inthe environment

Thus, conventional venue environmental systems are relatively static andreactive e.g., typically involving a maintenance person setting adesired temperature, and the venue temperature control system will worktowards achieving and maintaining the set temperature until a newtemperature is set by the maintenance person. A consequence of suchsystems are that manual intervention is often required in order toachieve occupant satisfaction. Furthermore, venues can often be verylarge (e.g., seat over 40,000 people) and consequently consumesignificant resources in connection with environmental regulation (e.g.,temperature, humidity, circulation, filtering, etc.) and achieving adesired set of environmental conditions can take hours. Maintenancepeople might not be aware of the number of occupants attending an eventat the venue let alone arrival time or occupant preferences or context.Innovations described herein leverage information gleaned through miningsocial networks to facilitate predicting size of crowds attending anevent, arrival times, occupant demographics, preferences, context, etc.Further to that mentioned above, in some embodiments, machine learningsystems can be explicitly or implicitly trained in accordance with thepresent invention. In other words, feedback can be employed over time,to analyze and learn from experiences based on an actual number anddemographics of attendees versus that predicted by the machine learningsystem. Accordingly, over time the machine learning systems (throughsuch training) can converge on higher confidence levels regarding apredicted of number of attendees and their respective demographics.

Accordingly, in some embodiments utilizing information obtained inconnection with an explicitly and/or implicitly trained model, a venue'senvironment can be regulated in advance to facilitate achievingappropriate environmental settings for the occupants as well asfacilitate efficient use of venue resources.

Some embodiments utilize machine learning systems that have beenexplicitly or implicitly trained to learn, based on determined and/orinferred occupant comfort, and can dynamically regulate environmentalconditions of the venue to facilitate achieving occupant comfort. Manyfactors can be taken into consideration in connection with regulatingvenue environmental conditions in accordance with the present invention.For example, and as will be described in greater detail below, suchfactors include (without limitation): historical occupant comfortlevels, occupant preferences, occupant context, ambient conditionsinside and outside of the venue, venue or occupant state conditions,venue zones, multiple occupant preferences and comfort, occupant zones,and intersection areas of occupant zones.

The subject disclosure is directed to computer processing systems,computer-implemented methods, apparatus and/or computer program productsthat facilitate efficiently and automatically (e.g., without directhuman involvement) regulating venue environmental conditions utilizingmachine learning to help achieve occupant comfort. Humans are alsounable to perform the embodiments described here as they include, andare not limited to, performing, e.g., complex Markov processes, Bayesiananalysis, or other artificial intelligence based techniques based onprobabilistic analyses and evaluating electronic information indicativeof occupant comfort, determining whether countless multitudes ofprobability values assigned to occupant comfort exceed or fall belowvarious probability values.

The computer processing systems, computer-implemented methods, apparatusand/or computer program products employ hardware and/or software tosolve tangible problems that are highly technical in nature e.g., anautomated processing, determining and/or inferring occupant comfort. Forexample, a human, or even thousands of humans, cannot efficiently,accurately and effectively manually apply countless thousands ofoccupant comfort variables to input points and perform analysis todetermine that a probability value assigned to an occupant comfort levelexceeds a defined probability value.

Various embodiments of the present invention can be employed inconnection with determinations and/or inferences. Furthermore, incertain instances trained machine learning models are utilized wheredeterminations are employed while in other instances inferences areemployed. In accordance with particular non-limiting implementations theterm “determine” (“determining”, etc.) is intended to mean ascertainwith a particular level of certainty, while the term “infer”(“inferring”, etc.) is intended to mean reach a conclusion by reasoningor deduction based on probabilistic-based analyses.

In order to provide for or aid in the numerous inferences describedherein (e.g. inferring occupant comfort), components described hereincan examine the entirety or a subset of data to which it is grantedaccess and can provide for reasoning about or inferring states of asystem, environment, etc. from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data.

Such inference can result in construction of new events or actions froma set of observed events and/or stored event data, whether or not theevents are correlated in close temporal proximity, and whether theevents and data come from one or several event and data sources. Variousclassification (explicitly and/or implicitly trained) schemes and/orsystems (e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines, etc.) can beemployed in connection with performing automatic and/or inferred actionin connection with the claimed subject matter.

A classifier can map an input attribute vector, x=(x1, x2, x3, x4, xn),to a confidence that the input belongs to a class, as byf(x)=confidence(class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to prognose or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hyper-surface in the space of possible inputs, where thehyper-surface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachesinclude, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

Social media data is typically noisy, format-free, of varying length,and multimedia. Furthermore, social relations among the entities, orsocial networks, form an inseparable part of social media data.Accordingly, various statistical and data mining methods implementedherein integrate social theories and research methods coincident withmining such types of complex social data. Some factors that areconsidered are: (1) exploiting characteristics of social media and useits multidimensional, multisource, and multisite data to aggregateinformation with sufficient statistics for effective mining; (2)obtaining sufficient samples, e.g., collect data is via applicationprogramming interfaces (APIs) from social media sites; (3) successfulnoise removal that addresses blind removal or noise as well as properlydefining what is noise; and (4) evaluating patterns in data. It is to beappreciated that any suitable set of graphs (e.g., null graphs, emptygraphs, directed/undirected/mixed graphs, weighted graphs, specialgraphs, complete graphs, planar graphs, bipartite graphs, regulargraphs, bridges, etc.) can be utilized in connection with buildingmodels that accurately predict, with high confidence, crowd sizeexpected at a venue. As noted above, the machine learning systems can beexplicitly or implicitly trained. For example, over time feedback can beemployed to analyze actual number and demographics of attendees versusthat predicted by the machine learning system. Accordingly, over timethe machine learning systems can converge on higher confidence levels ofprediction through such learning.

FIG. 1 illustrates a block diagram of an example, non-limiting system100 that facilitates utilizing machine learning (or probabilisticmodeling) to regulate environmental conditions of a venue to facilitateoccupant comfort in accordance with one or more embodiments describedherein. Aspects of systems (e.g., non-limiting system 100 and the like),apparatuses or processes explained in this disclosure can constitutemachine-executable component(s) embodied within machine(s), e.g.,embodied in one or more computer readable mediums (or media) associatedwith one or more machines. Such component(s), when executed by the oneor more machines, e.g., computer(s), computing device(s), virtualmachine(s), etc. can cause the machine(s) to perform the operationsdescribed.

In various embodiments, system 100 can be any type of mechanism,machine, device, facility, apparatus, and/or instrument that includes aprocessor and/or is capable of effective and/or operative communicationwith a wired and/or wireless network. Venues, machines, apparatuses,devices, facilities, and/or instrumentalities that can comprisenon-limiting system 100 can include, but are not limited to, concerthalls, theaters, arenas, wedding halls, auditoriums, movie theaters,recreational centers, banquet or meeting rooms, dining facilities,ships, airplanes, or any like large venue, . . . and can be implementedat least in part utilizing a variety of devices or appliances includingbut not limited to tablet computing devices, handheld devices, serverclass computing machines and/or databases, laptop computers, notebookcomputers, on-board vehicle computing devices or systems, desktopcomputers, cell phones, smart phones, consumer appliances and/orinstrumentation, industrial and/or commercial devices, hand-helddevices, digital assistants, multimedia Internet enabled phones,multimedia players, and the like.

The system 100 can include a bus 102 that can provide forinterconnection of various components of the system 100. It is to beappreciated that in other embodiments one or more system components cancommunicate wirelessly with other components, through a direct wiredconnection or integrated on a chipset. The system 100 can include one ormore sensors (e.g., sensors that detect temperature, pressure, light,image, humidity, pollution, smell, smoke, draft, moisture, air quality,particulate, accelerometers, vibration, noise, tone, weight, etc.) thatcollect information regarding environments external and/or internal to avenue, occupants and the venue itself and its associated equipment. Insome embodiments, a context component 106 can collect and providecontextual data regarding the venue and venue occupants. For example,context information such as what activity (e.g., running, swimming,having lunch, sleeping, drinking, etc.) in which an occupant wasengaging prior to entering the venue can be determined and/or inferred.Likewise, context information regarding activities of occupant(s) withinthe venue and/or operational information about the venue can becollected and provided to the system 100 for analysis in connection withregulating venue environment to facilitate occupant comfort.

The context component 106 can for example obtain context informationfrom many different sources e.g., an occupant cell phone, calendar,email, GPS, appliances, third parties, the venue, etc. The system 100can include a processor 108 and memory 110 that can carry outcomputational and storage operations of the system 100 as describedherein. A crowd estimation component 107 can search and analyzeinformation regarding potential occupants of the venue. The crowdestimation component 107 can, for example, search social media sites togather information (e.g., number of individuals planning on attending anevent at the venue, arrival times, activities conducted prior toarriving, type of transportation, demographics, what they will bewearing, comfort preferences, etc.) regarding potential occupants of thevenue. A communications component 112 can provide for transmitting andreceiving information, e.g., through one or more internal or externalnetworks 114 (wired or wireless networks). A comfort model component 116can be an explicitly and/or implicitly trained machine learningcomponent trained to determine and/or infer level of occupant comfortand can determine and/or infer adjustments to environment of the venueto facilitate occupant comfort. A comfort controller component 120 canregulate one or more venue components (e.g., air conditioner, heater,humidifier, de-humidifier, lights, stereo, noise cancellationcomponents, seats, pressure regulators, filters, pumps, motors, seatwarmers, seat coolers, etc.) based on output from the comfort modelcomponent 116 to facilitate achieving a venue environment conducive tooccupant comfort.

In an example, non-limiting implementation, a guest can enter a venue,and the context component 106 can determine from a mobile device of aguest (e.g., via a BLUETOOTH® or other wired or wireless connection)that the guest just finished running a marathon and through respectivewearable computing devices that the occupant has an elevated bodytemperature, and is exhausted. This context information can be sharedwith the comfort model component 116 which can perform aprobabilistic-based analysis on the context information as well asinformation from the sensor(s) 104, occupants respective preferences andhistorical data stored in memory, state information and other relevantinformation that have relevancy to occupant comfort. The comfort modelcomponent 116, based in part on the analyses, can output one or morerecommendations that can be utilized by the comfort controller component120 to adjust environmental conditions of the venue to facilitateachieving occupant comfort. For example, the comfort model component 116based on occupant context information regarding just completing themarathon, having elevated body temperatures, being fatigued can generatean inference that the occupants will need a cooler venue temperaturethan normal in order to be quickly cooled down and feel comfortable.Additionally, the comfort model component 116 can play relaxing music,and adjust lighting in the venue to effect a calming environment. Thecontext component 106, and sensors 104 can continually collect data thatis analyzed by the comfort model component 116 which will generatedeterminations or inferences regarding level of occupant comfort. Thecomfort controller component 120 can continually adjust venueenvironmental conditions to maintain occupant comfort. For example, asthe occupants are starting to cool down, the comfort controllercomponent 120 can adjust temperature by raising temperature slightly,reduce force of fans blowing air on occupant, change volume of music,change lighting, etc. Thus, the system 100 is adaptive and can employclosed or open-looped systems to facilitate maintaining occupant comforteven as conditions of the occupant change.

Additionally, capacity of the venue, zones of the venue, and number ofoccupants are analyzed in connection with regulating environmentalconditions. For example, if the venue has capacity for 20,000 people butthere will only be 200 occupants at a given time the system 100 canregulate environmental conditions of areas or zones that will betraversed and utilized by the occupants while setting unoccupied zonesat different levels in order to conserve resource and energyutilization. Furthermore, estimated arrival and departure times can befactored by the system 100 so that respective venue zones to be occupiedare regulated in advance of arrival and the same areas are regulated asa function of departure time.

The system 100 can also provide information bi-directionally. Forexample, sometimes occupants can be unsure as to what the temperature islike in a venue (e.g., a cold movie theater) and not know whether tobring a sweater or how to properly dress. The system 100 can providenotifications via the communications component 112 to occupants inadvance of an event (e.g., the temperature at the movie theater will be72 degrees) which will allow for the occupant to dress appropriately.

The system 100 includes a profile component 122 that builds and storesin memory 110 venue occupant comfort profiles. A season ticket holderassociated with a venue is commonly a frequent user of the venue, andthe comfort model component 116 can build a specific model for thepatron as well as respective models for other frequent occupants (e.g.,family members, close friends, . . . ). Upon identification of anoccupant having entered the venue, e.g., via facial recognition, seatingpattern (e.g., body contours, weight, etc.), biometrics, voicerecognition, iris recognition, cell phone, or any other suitable meansfor identification and authentication, the profile component 122 canaccess specific profiles for each occupant of the venue that can beutilized by the comfort model component 116 to generate determinationsor inferences regarding occupant comfort level and generatingrecommendations to the comfort controller component 120 to adjustenvironment of the venue including respective zones to achieveoccupant(s) comfort. It is to be appreciated that when multipleoccupants are in the venue, their respective profiles may conflict incertain aspects, e.g., temperature preference, volume preference, musicpreference, lighting preference, etc.

The comfort model component can utilize the respective profiles andspecific occupant models to achieve a happy medium that achieves levelsof comfort suitable for most or all occupants. A utility-based analysiscan also be employed where the costs of taking a certain action areweighed against the benefits. For example, if a lecturer desires thathis students have a high level of attention, the system 100 can regulatevenue environment (e.g., a bit cooler) to facilitate focus of attention.The comfort model component 116 can also generate recommendations to thecomfort controller to adjust environment of respective zones of thevenue differently based on occupants within each zone.

Likewise, if the event is a wedding the system 100 can adjustenvironmental conditions to account for changing themes at the event.For example, the environment may be set at a certain point during acocktail reception where people are standing and mingling, then a secondsetting when seated for dinner, and yet a third setting when occupantsare dancing after dinner. The system 100 based on contextual informationgathered regarding occupants, the event, zones, preferences, activitiescan dynamically adjust venue environment to facilitate achievingcontinued occupant comfort as well as efficient utilization of venueresources.

In an embodiment, the system 100 includes a pattern recognitioncomponent 126. A set of the sensors 104 can include cameras that collectimage data inside and outside of the venue. The pattern recognitioncomponent 126 can be employed to identify occupants, collect facialexpression information that can be analyzed by the comfort modelcomponent 116 to assess state of occupant(s), e.g., tired, hot, cold,sleepy, alert, sad, happy, nervous, stressed, etc. Based on suchdetermination or inference, the comfort model component 116 can generaterecommendations to the comfort controller component 120 to adjust venueenvironment. Additionally, the pattern recognition component 126 canfacilitate determining venue conditions, e.g., temperature, humiditylevels, etc. The pattern recognition component 126 can detect outsideenvironmental activity, e.g., traffic, weather, pedestrians, movingobjects, etc. that can be utilized by the comfort model component 116 toinstruct the venue to regulate environment.

With reference now to FIGS. 1 and 2 in conjunction, an example,non-limiting, schematic representation of a venue interior 200 isillustrated. The interior 200 includes a set of occupant seats inrespective zones 101-118, A-K, and the floor that can include sensors208 (e.g., weight, temperature, moisture, etc.) and environmentalcomfort components (e.g., seat heaters, coolers, massage components,seat adjustment components, vents, blowers, air conditioning, heaters,ventilation, etc.) that can be regulated by the comfort model component116 to facilitate occupant comfort. Sensors 208 can also be situatedabout the venue to collect other information e.g., video information.Vents/blowers (not depicted) can be situated throughout the venue tofacilitate temperature regulation as well as humidity. Additionalsensors (not depicted) can be located at various locations within andoutside of the venue. Sensors can collect information regarding state ofthe venue and/or occupants (e.g., temperature, light, moisture,pressure, weight, ice formation, noise, etc.). As discussed above, thevenue can be divided into respective zones (e.g., 101-118, A-K, and thefloor) and information regarding the respective zones and occupantstherein can be collected (e.g., via sensors 104, 208 and contextcomponent 106) and the collected information analyzed by the comfortmodel component 116 to infer or determine respective occupant comfort inthe zones. Based on the inference or determination, the comfort modelcomponent can direct the comfort controller component 120 to adjustenvironmental conditions (e.g., seat position, seat temperature, blowerintensity, zone temperature, zone moisture level, zone noise level,music volume, zone lighting, massage, wireless signal strength, airconditioning, heating, ventilation, etc.) in respective zones tofacilitate occupant comfort in each zone. For example, a family entersthe venue 200 and the context component 106 can determine that thefamily is attending a hockey game. The sensors 104, 208 along withpattern recognition component 126 can facilitate identifying whichfamily member is seated in a particular seat. The comfort modelcomponent 116 can utilize profiles generated by the profile component122 to generate inferences and determinations regarding suitableenvironmental conditions for each passenger in their respective zones tofacilitate comfort for each family member. Based in part on output fromthe comfort model component 116, the comfort controller component 120can adjust the zone or sub-section thereof respectively to facilitateoccupant comfort, e.g., regulate temperature and humidity in each zone,control volume, wireless strength, etc. per aggregated occupantpreference. The system 100 can continually monitor occupant state,context and comfort levels and dynamically adjust environmentalconditions. For instance, if the occupants are inferred or determined tobe losing interest, the comfort controller component 120 can decreasetemperature, increase lighting, increase volume, etc. to facilitatemaintaining occupant alertness or focus of attention.

It is to be appreciated that various example, non-limiting body andcontextual parameters (e.g., blood pressure, heart rate, pulse, skintemperature, respiratory rates, skin humidity, blood oxygen saturation,in/out venue air temperature, in/out venue humidity, venue lightintensity, venue noise levels) that can be utilized to assess occupantcomfort and regulate venue comfort facilities. A model can be built forinferring individual occupant comfort degree based on his/her bodyparameters and contextual parameters, and the model can also infer ordetermine comfort of a group of occupants as well. The model can analyzecontextual parameters that damage occupant(s) comfort when occupant(s)analyzed are determined or inferred to be un-comfortable. Venuefacilities can be adjusted appropriately to improve upon the damagingcontextual parameters and occupant comfort degree change can be utilizedas feedback for optimization.

In one example, the entire venue (e.g., all zones) are predicted to beoccupied. The system 100 can adjust venue environment for all zones tofacilitate occupant comfort upon expected arrival time. In anotherexample, only zone B will be utilized for an event. In such smallerevent instance, the system can keep temperatures other than zone B andassociated ingresses and egresses at higher, unoccupied environmentallevels and focus on environmental regulation in connection with zone Bto afford comfort to the occupants therein.

FIG. 3 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 300 that facilitates venue occupant comfortin accordance with one or more embodiments described herein. Themethodology can be employed in connection with dynamic action and/orpredictive action. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity.

At 302, ambient conditions associated with exterior and interiorconditions of a venue are sensed, including but not limited to sensedinformation associated with the venue and/or occupants of the venue. Forexample, sensed information may include one or more of ambienttemperature, light levels, odors, smoke, pollutants, allergens,pressure, moisture, sound levels, noise levels, and weather conditions.

At 304, occupant context can be determined or inferred (e.g., via acontext component 106). For example, occupant context can include prioractivities, current activities, stress level, emotional state,destination, upcoming events, attire, alertness, fatigue level,sleepiness level, focus of attention, mood, health state, etc. Extrinsicor intrinsic information regarding an occupant can be collected from avariety of sources (e.g., cell phones, social network sites, GPS, email,text messages, calendars, appointments, wearable computing devices,cloud-based services, 3rd parties, the venue, etc.).

At 306 the sensed information and occupant context information areanalyzed using a trained machine learning system (e.g., comfort modelcomponent 116).

At 308 a determination or inference is made regarding whether or not theoccupant is comfortable. For example, a comfort index can be utilized toassess whether the occupant is comfortable or if comfort can be improvedupon. If Yes, the process returns to 302. If No, at 310 an environmentalregulation system (e.g., comfort controller component 120) is utilizedto adjust environmental conditions until the occupant is deemed orinferred to be comfortable.

FIG. 4 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 400 that facilitates venue occupant comfortin accordance with one or more embodiments described herein. Themethodology can be employed in connection with dynamic action and/orpredictive action.

At 404, an inference or determination is made regarding occupant arrivaltimes (e.g., using crowd estimation component 107).

At 406, a prediction is made, based at least in part on social networkinformation regarding the size of crowd expected to attend an event(e.g., using crowd estimation component 107). In some embodiments,sensed ambient conditions, occupant context information and eventcontext using a trained machine learning system are also factored in(e.g., using comfort model component 116).

At 408, adjustments are made to the venue environment (e.g., usingcomfort controller component 120).

For simplicity of explanation, the computer-implemented methodologiesare depicted and described as a series of acts. It is to be understoodand appreciated that the subject innovation is not limited by the actsillustrated and/or by the order of acts, for example acts can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts can berequired to implement the computer-implemented methodologies inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the computer-implementedmethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be further appreciated that the computer-implementedmethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such computer-implemented methodologies tocomputers. The term article of manufacture, as used herein, is intendedto encompass a computer program accessible from any computer-readabledevice or storage media.

In order to provide additional context for various aspects of thedisclosed subject matter, FIG. 5 as well as the following discussion areintended to provide a general description of a system 500 in whichvarious aspects of the disclosed embodiments shown and described inconnection with FIGS. 1-4 can be implemented. In some embodiments, forexample, system 500 can implement a method stored in memory and/or othercomputer readable/executable storage such as a computer program product(an example of which is described in more detail below) that facilitatesvenue occupant comfort in accordance with one or more embodimentsdescribed herein. The methodology can be employed in connection withdynamic action and/or predictive action. Ambient conditions associatedwith exterior and interior conditions of a venue are sensed, includingbut not limited to sensed information associated with the venue and/oroccupants of the venue. For example, sensed information may include oneor more of ambient temperature, light levels, odors, smoke, pollutants,allergens, pressure, moisture, sound levels, noise levels, and weatherconditions. Occupant context can be determined or inferred. For example,occupant context can include prior activities, current activities,stress level, emotional state, destination, upcoming events, attire,alertness, fatigue level, sleepiness level, focus of attention, mood,health state, etc. Extrinsic or intrinsic information regarding anoccupant can be collected from a variety of sources (e.g., cell phones,social network sites, GPS, email, text messages, calendars,appointments, wearable computing devices, cloud-based services, 3rdparties, the venue, etc.). The sensed information and occupant contextinformation are analyzed using a trained machine learning system. Adetermination or inference is made regarding whether or not the occupantis comfortable. For example, a comfort index can be utilized to assesswhether the occupant is comfortable or if comfort can be improved upon.If Yes, the methodology is repeated. If No, an environmental regulationsystem is utilized to adjust environmental conditions until the occupantis deemed or inferred to be comfortable.

With reference now to FIG. 5, a suitable operating environment 501 forimplementing various aspects of this disclosure can also include acomputer 512. The computer 512 can also include a processing unit 514, asystem memory 516, and a system bus 518. The system bus 518 couplessystem components including, but not limited to, the system memory 516to the processing unit 514. The processing unit 514 can be any ofvarious available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit514. The system bus 518 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI). The system memory 516 can alsoinclude volatile memory 520 and nonvolatile memory 522. The basicinput/output system (BIOS), containing the basic routines to transferinformation between elements within the computer 512, such as duringstart-up, is stored in nonvolatile memory 522. By way of illustration,and not limitation, nonvolatile memory 522 can include read only memory(ROM), programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory 520 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 512 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 5 illustrates, forexample, a disk storage 524. Disk storage 524 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 524 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 524 to the system bus 518, a removable ornon-removable interface is typically used, such as interface 526. FIG. 5also depicts software that acts as an intermediary between users and thebasic computer resources described in the suitable operating environment501. Such software can also include, for example, an operating system528. Operating system 528, which can be stored on disk storage 524, actsto control and allocate resources of the computer 512. Systemapplications 530 take advantage of the management of resources byoperating system 528 through program modules 532 and program data 534,e.g., stored either in system memory 516 or on disk storage 524. It isto be appreciated that this disclosure can be implemented with variousoperating systems or combinations of operating systems. A user enterscommands or information into the computer 512 through input device(s)536. Input devices 536 include, but are not limited to, a pointingdevice such as a mouse, trackball, stylus, touch pad, keyboard,microphone, joystick, game pad, satellite dish, scanner, TV tuner card,digital camera, digital video camera, web camera, and the like. Theseand other input devices connect to the processing unit 514 through thesystem bus 518 via interface port(s) 538. Interface port(s) 538 include,for example, a serial port, a parallel port, a game port, and auniversal serial bus (USB). Output device(s) 540 use some of the sametype of ports as input device(s) 536. Thus, for example, a USB port canbe used to provide input to computer 512, and to output information fromcomputer 512 to an output device 540. Output adapter 542 is provided toillustrate that there are some output devices 540 like monitors,speakers, and printers, among other output devices 540, which requirespecial adapters. The output adapters 542 include, by way ofillustration and not limitation, video and sound cards that provide ameans of connection between the output device 540 and the system bus518. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)544.

Computer 512 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)544. The remote computer(s) 544 can be a computer, a server, a router, anetwork PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 512.For purposes of brevity, only a memory storage device 546 is illustratedwith remote computer(s) 544. Remote computer(s) 544 is logicallyconnected to computer 512 through a network interface 548 and thenphysically connected via communication connection 550. Network interface548 encompasses wire and/or wireless communication networks such aslocal-area networks (LAN), wide-area networks (WAN), cellular networks,etc. LAN technologies include Fiber Distributed Data Interface (FDDI),Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL). Communication connection(s) 550 refersto the hardware/software employed to connect the network interface 548to the system bus 518. While communication connection 550 is shown forillustrative clarity inside computer 512, it can also be external tocomputer 512. The hardware/software for connection to the networkinterface 548 can also include, for exemplary purposes only, internaland external technologies such as, modems including regular telephonegrade modems, cable modems and DSL modems, ISDN adapters, and Ethernetcards.

Embodiments of the present invention may be a system, a method, anapparatus and/or a computer program product at any possible technicaldetail level of integration. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention. The computer readable storage mediumcan be a tangible device that can retain and store instructions for useby an instruction execution device. The computer readable storage mediumcan be, for example, but is not limited to, an electronic storagedevice, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium can alsoinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of various aspects of thepresent invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to customize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and number-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms (such as in the examplesprovided above). Additionally, the disclosed memory components ofsystems or computer-implemented methods herein are intended to include,without being limited to including, these and any other suitable typesof memory.

What has been described above include mere examples of systems, computerprogram products and computer-implemented methods. It is, of course, notpossible to describe every conceivable combination thereof for purposesof describing this disclosure, but one of ordinary skill in the art canrecognize that many further combinations and permutations are possible.Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transition word in a claim. The descriptions of thevarious embodiments have been presented for purposes of illustration,but are not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen tobest explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method, comprising:sensing, using one or more sensors, ambient conditions associated withconditions of a venue; inferring number of occupants expected to occupythe venue and estimated arrival and departure times; inferring contextof an occupant of the venue; analyzing information from the groupconsisting of: one or more of the sensed ambient conditions, inferrednumber of occupants expected to occupy the venue and estimated arrivaland departure times, and inferred occupant context information using acomfort model component trained on occupant comfort related data andprior accuracy of inferred crowd size versus actual crowd size thatoccupied the venue; building comfort profiles for high frequency usersof the venue, wherein at least one of the comfort profiles contains oneor more comfort preferences of a user learned from social media datamining, wherein the one or more comfort preferences comprise at leastone of temperature preference, volume preference, music preference, orlighting preference; and adjusting one or more environmental conditionsof a zone of the venue in response to the analyzed ambient conditions,the occupant context information, and the comfort profiles.
 2. Thecomputer-implemented method of claim 1, further comprising sensingoccupant body temperature.
 3. The computer-implemented method of claim1, further comprising inferring selected from a group consisting of:occupant action prior to entering the venue; amount of clothing worn bythe occupant, and an optimal environmental condition suitable for amajority of occupants of the venue.
 4. The computer-implemented methodof claim 1, further comprising employing facial recognition using apattern recognition component to infer a comfort level of at least oneof the one or more occupants, and wherein the inference is based on oneor more facial expressions.
 5. A computer-implemented method,comprising: sensing, by one or more first sensors, ambient conditions ofa venue; inferring, by one or more second sensors, context of one ormore occupants in the venue, including at least occupant activity in thevenue and occupant attire; identifying, by image pattern recognition,the one or more occupants; building comfort profiles for the one or moreoccupants, wherein at least one of the comfort profiles contains one ormore comfort preferences of an identified occupant learned via socialmedia data mining, wherein the one or more comfort preferences compriseat least one of temperature preference, volume preference, musicpreference, or lighting preference; analyzing the ambient conditions,the inferred occupant context, and the occupant comfort profiles by acomfort model component trained on occupant comfort related data todetermine a strategy to adjust one or more environmental conditions of azone in the venue to improve aggregate comfort of the one or moreoccupants; and adjusting the one or more environmental conditions of thezone in the venue based on the determined strategy.
 6. Thecomputer-implemented method of claim 5, further comprising employingfacial recognition using a pattern recognition component to infer acomfort level of at least one of the one or more occupants, based on oneor more facial expressions of the at least one of the one or moreoccupants.
 7. The computer-implemented method of claim 5, wherein theinferred occupant context includes occupant action prior to entering thevenue.
 8. The computer-implemented method of claim 5, wherein thecomfort preferences of the occupant comfort profiles include an airtemperature preference, and wherein the one or more environmentalconditions adjusted according to the determined strategy include an airtemperature in the zone in the venue.
 9. The computer-implemented methodof claim 5, wherein the comfort preferences of the occupant comfortprofiles include a noise volume preference, and wherein the one or moreenvironmental conditions adjusted according to the determined strategyinclude a noise volume in the zone in the venue.
 10. Thecomputer-implemented method of claim 5, wherein the comfort preferencesof the occupant comfort profiles include a lighting intensitypreference, and wherein the one or more environmental conditionsadjusted according to the determined strategy include a lightingintensity in the zone in the venue.
 11. The computer-implemented methodof claim 5, wherein the comfort preferences of the occupant comfortprofiles include a music genre preference, and wherein the one or moreenvironmental conditions adjusted according to the determined strategyinclude a music genre in the zone in the venue.
 12. Thecomputer-implemented method of claim 5, wherein the inferred occupantcontext includes at least one of blood pressure, heart rate, breathingrate, body temperature, or blood oxygen saturation, measured via one ormore biometric sensors.
 13. A computer program product, the computerprogram product comprising a non-transitory computer readable storagemedium having program instructions embodied therewith, the programinstructions executable by a processor to cause the processor to: sense,by one or more first sensors, ambient conditions of a venue; infer, byone or more second sensors, context of one or more occupants in thevenue, including at least occupant activity in the venue and occupantattire; identify, by image pattern recognition, the one or moreoccupants; build comfort profiles for the one or more occupants, whereinat least one of the comfort profiles contains one or more comfortpreferences of an identified occupant learned via social media datamining, wherein the one or more comfort preferences comprise at leastone of temperature preference, volume preference, music preference, orlighting preference; analyze the ambient conditions, the inferredoccupant context, and the occupant comfort profiles by a comfort modelcomponent trained on occupant comfort related data to determine astrategy to adjust one or more environmental conditions of a zone in thevenue to improve aggregate comfort of the one or more occupants; andadjust the one or more environmental conditions of the zone in the venuebased on the determined strategy.
 14. The computer program product ofclaim 13, wherein the computer executable instructions further cause theprocessor to employ facial recognition using a pattern recognitioncomponent to infer a comfort level of at least one of the one or moreoccupants, based on one or more facial expressions of the at least oneof the one or more occupants.
 15. The computer program product of claim13, wherein the inferred occupant context includes occupant action priorto entering the venue.
 16. The compute program product of claim 13,wherein the comfort preferences of the occupant comfort profiles includean air temperature preference, and wherein the one or more environmentalconditions adjusted according to the determined strategy include an airtemperature in the zone in the venue.
 17. The computer program productof claim 13, wherein the comfort preferences of the occupant comfortprofiles include a noise volume preference, and wherein the one or moreenvironmental conditions adjusted according to the determined strategyinclude a noise volume in the zone in the venue.
 18. The computerprogram product of claim 13, wherein the comfort preferences of theoccupant comfort profiles include a lighting intensity preference, andwherein the one or more environmental conditions adjusted according tothe determined strategy include a lighting intensity in the zone in thevenue.
 19. The computer program product of claim 13, wherein the comfortpreferences of the occupant comfort profiles include a music genrepreference, and wherein the one or more environmental conditionsadjusted according to the determined strategy include a music genre inthe zone in the venue.
 20. The computer program product of claim 13,wherein the inferred occupant context includes at least one of bloodpressure, heart rate, breathing rate, body temperature, or blood oxygensaturation, measured via one or more biometric sensors.